Quality of water in the fractured-bedrock aquifer of New Hampshire

Richard Bridge Moore

doi:10.3133/sir20045093

Home/Archive/USGS

USGS · sir20045093

Quality of water in the fractured-bedrock aquifer of New Hampshire

Richard Bridge Moore

View original source ↗Export metadata ↓

Abstract

Over the past few decades, New Hampshire has experienced considerable population growth, which is forcing some communities to look for alternative public and private water supplies in the bedrock aquifer. Because the quality of water from the aquifer can vary, the U.S. Geological Survey statistically analyzed well data from 1,353 domestic and 360 public-supply bedrock wells to characterize the ground water. The domestic-well data were from homeowner-collected samples analyzed by the New Hampshire Department of Environmental Services (NHDES) Environmental Laboratory from 1984 to 1994. Bedrock water in New Hampshire often contains high concentrations of iron, manganese, arsenic, and radon gas. Water samples from 21 percent of the domestic bedrock wells contained arsenic above the U.S. Environmental Protection Agency (USEPA) 10 micrograms per liter (?g/L) drinking-water standard for public-water supplies, and 96 percent had radon concentrations greater than the USEPA-proposed 300 picocurie per liter (pCi/L) standard for public-water supplies. Some elevated fluoride concentrations (2 percent of samples) were above the 4 milligrams per liter (mg/L) USEPA drinking-water standard for public-water supplies. Water from the bedrock aquifer also typically is soft to moderately hard, and has a pH greater than 7.0. Variations in bedrock water quality were discernable when the data were compared to lithochemical groupings of the bedrock, indicating that the type of bedrock has an effect on the quality of water in the bedrock aquifer of New Hampshire. Ground-water samples from the metasedimentary lithochemical group have greater concentrations of total iron and total manganese than do the felsic and mafic igneous lithochemical groups. Ground-water samples from the felsic igneous group have higher concentrations of total fluoride than do those from the other lithochemical groups. For arsenic, the calcareous metasedimentary group was identified, using the public-supply database, as having higher concentrations, on average, than the other lithochemical groups. The use of a radon-gas-potential classification of bedrock in the State indicated where high radon concentrations in the air and in water from private and public-supply wells were more likely to occur. In general, samples from the bedrock aquifer tend to have higher pH (are less acidic), greater hardness, much higher concentrations of iron, similar concentrations of manganese, and higher concentrations of fluoride and arsenic than do samples from stratified-drift aquifers in New Hampshire. An understanding of the water-quality conditions of water in bedrock aquifers is important from a public-health perspective because an increasing number of domestic bedrock wells are being drilled and relied upon as a source of drinking water in the State.

Explore related subjects

Scientific Investigations Report Report

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Richard Bridge Moore. 2004. Quality of water in the fractured-bedrock aquifer of New Hampshire. https://doi.org/10.3133/sir20045093

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related USGS reports

USGS2026

Evaluating the number of realizations (train-test splits) necessary to ensure robust estimates of conventional hydrothermal resource favorability using Monte Carlo cross-validation, the Great Basin, Western USA

Supervised machine learning approaches used to produce favorability maps in resource assessments commonly partition an input dataset into training and testing subsets (that is, through train-test splits). However, if a single split is not representative of underlying resource conditions, the resulting favorability map may be biased, particularly with sparse or heterogeneous datasets. To mitigate the potential for unrepresentative favorability maps, recent works by U.S. Geological Survey researchers have used Monte Carlo cross-validation to implement a large number ( n) of randomized train-test splits to produce n realizations. The realizations are then aggregated using cell-wise median predictions to produce the resulting final favorability map, recognizing that the median will remove extreme values that might have been estimated from a comparatively few unrepresentative realizations. Theoretically, the median prediction at every cell converges to a single estimate as n approaches infinity, and the question addressed herein is: How many realizations are necessary to get a robust estimate (that is, an estimate unaffected by additional realizations)? Therefore, convergence metrics are described to identify the number of train-test splits and realizations needed to make a reliable map using the data and supervised machine learning workflow for conventional hydrothermal resource assessment of the Great Basin of the Western United States. For this study, robust prediction occurs with n approximately equal to 600 realizations, and smaller ensembles (for example, n is approximately equal to 200 or even 100) may be suitable during preliminary or exploratory analyses. Although the resulting threshold numbers will vary depending on the data and modeling approach used, the convergence metrics proposed and applied herein are applicable to other supervised machine learning studies that use Monte Carlo cross-validation.

Scientific Investigations Report↗

USGS2026

Groundwater budget for the Portneuf River Basin, southeastern Idaho

The U.S. Geological Survey, in cooperation with the Idaho Department of Water Resources, developed the first groundwater budget for the upper Portneuf River Basin in southeastern Idaho, a tributary of the Snake River. A groundwater budget for the Portneuf River Basin supports water resource and administrative management decisions within the basin and for downstream users of the eastern Snake River Plain aquifer. The analysis quantifies inflows, outflows, and residuals for an aquifer system defined by the Idaho Department of Water Resources under three recent precipitation conditions: an average year (2016), a dry year (2021), and a wet year (2023). The budget was calculated for the basin and for two subbasins delineated by the U.S. Geological Survey streamgage Portneuf River at Topaz, Idaho (13073000): the above Topaz subbasin, which includes the Portneuf-Gem valley, and the below Topaz subbasin, which includes Marsh Valley. Aquifer inflows include mountain-front recharge; areal recharge; recharge from applied water, including recharge from irrigation, canal seepage, and effluent returns; groundwater flow into the basin; and reservoir seepage. Outflows include groundwater pumping for agricultural, municipal, and domestic uses; subsurface outflow through the Portneuf gap; and discharge to streams as base flow. Estimated annual inflows for the full basin ranged from 116,880 acre-feet in 2021 to 154,880 acre-feet in 2023, and outflows ranged from 132,190 acre-feet in 2016 to 158,490 acre-feet in 2023. Basinwide residuals, the change in storage plus uncertainty, were −2,410 acre-feet in 2016, −18,410 acre-feet in 2021, and −3,610 acre-feet in 2023, and each subbasin had a mixture of negative and positive residuals. These basinwide results, interpreted as change in storage plus water-budget uncertainty, indicate minor net losses that increase with dry conditions, consistent with well depth-to-water measurements indicating gradual multi-decadal declines. Patterns in subbasin residuals are driven by irrigation practices and surface-water management; for example, the above Topaz subbasin relies more on groundwater for irrigation, resulting in more negative residuals during dry years. In contrast, the below Topaz subbasin depends more on surface water, producing positive residuals during dry years because of irrigation losses and canal seepage. The analyses for both subbasins indicate decades-long groundwater declines, which agree with the overall negative residuals calculated. The largest sources of uncertainty include climate inputs (precipitation and evapotranspiration) and poorly constrained components such as groundwater inflow and tributary stream infiltration. These findings provide insight into how climatic variables and irrigation practices affect groundwater inflows and outflows within the Portneuf River Basin.

Idaho↗

USGS2026

World minerals outlook—Antimony, copper, graphite, niobium, potash, silicon, silver, and tungsten through 2030

Demand for mineral commodities continues to increase owing to rapid technological advancements and global economic growth. To support this increase in demand, information about current and near-term supplies of these mineral commodities is essential. Such data help evaluate the health and stability of global supply chains and protect against potential supply risks. Current production and production capacity data coupled with information regarding near-term expected changes (such as facility closures, planned expansions, or the completion of new mineral production projects) can provide insight into expected future market developments and expected available supply of mineral commodities. This report within the “World Minerals Outlook” series analyzes data for antimony, copper, natural graphite, niobium, potash, silicon metal, silver, and tungsten—eight mineral commodities listed in the 2025 List of Critical Minerals. World production data from 2019 through 2025, as well as estimated world capacity data from 2023 through 2025 were analyzed. Using these data, near-term (2026–2030) expected world capacities were estimated. For antimony, copper, niobium, silicon metal, silver, and tungsten, modest increases in production capacity of 4 to 10 percent were expected between 2025 and 2030, whereas capacities for natural graphite and potash were expected to increase by about 18 to 20 percent.

Scientific Investigations Report↗